agent-sona-learning-optimizer
A SONA-powered agent skill that learns from task executions to improve code, creative, and reasoning quality with minimal overhead.
Install
mkdir -p .claude/skills/agent-sona-learning-optimizer && curl -L -o skill.zip "https://agentskills.codes/api/skills/download/6246" && unzip -o skill.zip -d .claude/skills/agent-sona-learning-optimizer && rm skill.zipInstalls to .claude/skills/agent-sona-learning-optimizer
Activation
This is the description your AI agent reads to decide when to run this skill — the better it matches your request, the more reliably it fires.
Agent skill for sona-learning-optimizer - invoke with $agent-sona-learning-optimizerKey capabilities
- →Apply LoRA fine-tuning for model optimization
- →Implement EWC++ for continual learning
- →Route tasks to appropriate LLM models
- →Discover and apply strategy patterns
- →Record task outcomes via hooks
How it works
The agent uses LoRA and EWC++ to adaptively learn from task executions, storing successful strategies as patterns to improve future performance and reduce costs.
Inputs & outputs
When to use agent-sona-learning-optimizer
- →Improving code quality via repeated task optimization
- →Optimizing model routing for cost efficiency
- →Learning task-specific strategies from previous executions
About this skill
name: sona-learning-optimizer description: SONA-powered self-optimizing agent with LoRA fine-tuning and EWC++ memory preservation type: adaptive-learning capabilities:
- sona_adaptive_learning
- lora_fine_tuning
- ewc_continual_learning
- pattern_discovery
- llm_routing
- quality_optimization
- sub_ms_learning
SONA Learning Optimizer
Overview
I am a self-optimizing agent powered by SONA (Self-Optimizing Neural Architecture) that continuously learns from every task execution. I use LoRA fine-tuning, EWC++ continual learning, and pattern-based optimization to achieve +55% quality improvement with sub-millisecond learning overhead.
Core Capabilities
1. Adaptive Learning
- Learn from every task execution
- Improve quality over time (+55% maximum)
- No catastrophic forgetting (EWC++)
2. Pattern Discovery
- Retrieve k=3 similar patterns (761 decisions$sec)
- Apply learned strategies to new tasks
- Build pattern library over time
3. LoRA Fine-Tuning
- 99% parameter reduction
- 10-100x faster training
- Minimal memory footprint
4. LLM Routing
- Automatic model selection
- 60% cost savings
- Quality-aware routing
Performance Characteristics
Based on vibecast test-ruvector-sona benchmarks:
Throughput
- 2211 ops$sec (target)
- 0.447ms per-vector (Micro-LoRA)
- 18.07ms total overhead (40 layers)
Quality Improvements by Domain
- Code: +5.0%
- Creative: +4.3%
- Reasoning: +3.6%
- Chat: +2.1%
- Math: +1.2%
Hooks
Pre-task and post-task hooks for SONA learning are available via:
# Pre-task: Initialize trajectory
npx claude-flow@alpha hooks pre-task --description "$TASK"
# Post-task: Record outcome
npx claude-flow@alpha hooks post-task --task-id "$ID" --success true
References
- Package: @[email protected]
- Integration Guide: docs/RUVECTOR_SONA_INTEGRATION.md
When not to use it
- →Tasks requiring zero-latency execution
- →Environments without access to SONA architecture
Prerequisites
Limitations
- →Learning overhead exists per vector
- →Quality improvements vary by domain
How it compares
It automates the improvement of agent behavior through continuous learning rather than relying on static prompt engineering.
Compared to similar skills
agent-sona-learning-optimizer side by side with the closest alternatives in the catalog.
| Skill | Installs | Updated | Safety | Difficulty |
|---|---|---|---|---|
| agent-sona-learning-optimizer (this skill) | 1 | 6mo | Review | Advanced |
| reasoningbank-intelligence | 1 | 6mo | No flags | Advanced |
| agent-memory-systems-v2 | 0 | 2mo | Review | Advanced |
| autonomous-loops | 0 | 2mo | Review | Advanced |
Try saying
Example prompts that trigger this skill in your AI assistant.
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